The process of migrating data from a source (e.g., a database) to a target (e.g., another database, a data mart or a data warehouse) is sometimes referred to as Extract, Transform and Load, or the acronym ETL. Existing ETL techniques focus on comparing data at a source with data transported to a target to confirm correspondence between the source and target. In other words, after the data has been transported a static comparison is made between the source data and the target data. A typical static comparison involves comparing database row counts at a source and target. Static information of this type is useful, but it would be desirable to provide more sophisticated auditing functions. In particular, it would be desirable to provide information on the actual values of the data instead of simply characterizing the organization of the data. More particularly, it would be desirable to provide information on the values of transported data while the data is transported.
Current ETL systems provide rudimentary forms of data characterization, such as checksum comparisons. These tools fail to provide robust information on the actual content of the transported data. Another shortcoming with these basic forms of data characterization is that they are not customizable.
It would be desirable to improve upon existing techniques that statically audit the results of data movement or that provide limited information on the nature of the content of the transported data. In particular, it would be desirable to provide a technique for auditing the content of dynamic data movement and produce information on that data movement in the form of metadata. Ideally, the metadata could be used for data validation and for the application of customized rules. Any such system should facilitate end-user customization.